The art of computing has advanced greatly since the days of Charles Babbage. The innovations of Turing, von Neumann and their like have been overshadowed in recent years by the development of what has come to be described as machine intelligence.
In the field of machine intelligence, the concept of an active data store (sometimes referred to as an intelligent data store or database) is known. An active data store is one in which a rules engine or an equivalent active component can apply rules or event-based triggers to data and thereby modify that data in some way based on inferences drawn from the combination of data and rules.
Active data stores may take the form of simple file systems such as are found in an operating system's file system in personal computers. These may also be organized as hierarchical database systems controlled by a hierarchical database management system, or as relational, object-oriented or text databases.
To take a simple example, consider a data store in the form of a database storing data about family relationships. If a new data item representing a new-born child is added to the database, it might be related to existing data about a person thus:
Bob is-a-child-of Alice
The rules engine responds to the arrival of this new data by examining the assertion that Bob is a child of Alice. It examines its store of rules, and finds that a rule exists that if Bob is a child of Alice, Alice must be a parent of Bob. It locates and examines the data for the entity Alice, but finds no such assertion in the data. It therefore triggers a change to the entity Alice to include the assertion:
Alice is-a-parent-of Bob
Originally, active data stores were used experimentally and in relatively closed environments; a typical example was a rule-based active database to allow pharmaceutical products to be selected for a patient, using predetermined rules to take into consideration any contraindications and possible conflicts between drugs prescribed for a patient. The data store was typically not self-modifying; that is, the rules were not used to cause changes to the data, but only to select and extract data. The data store thus contained very stable data. The rules engines of these systems were designed to select data by traversing the data store following a tree-structured rules hierarchy, and thereby to satisfy a user's query.
It is well-known in the art to have entity-relationship databases for storing data in the form of networks of relationships and deriving information from the database, for example, by using visual navigation means. In such databases, relationships and entities are provided with attributes, and these attributes in turn can be used to derive information from the database. Conventionally, it is possible to use rule based processing to examine relationships, entities and attributes and derive further facts, such as higher and lower level structural relationship information therefrom.
More recently, active data stores have been implemented to interact with applications and with middleware, such as transaction processing monitors and workflow processing systems. Also, the ability of active data stores to modify data and to propagate modifications to both data and rules, based on the cascading of rule-based changes, has been enhanced. The use of active data stores has become increasingly important in information-based and service-provider industries in which knowledge management is a central feature of the business. An example of this is in the area of provision of computer solutions, comprising hardware and software, in which the provider adds value by incorporating industry-specific know-how in optimizing the operation of the total solution. By way of example, consider a computer solutions provider who enters a bid to supply a complete business management solution to the insurance industry. The experience of previous engagements in the same industry and of the integration techniques utilized to provide the best possible combination of hardware and software can now be captured and stored in a data store, and retrieved for each new engagement, such that the system architect and the system builders do not need to reinvent or rediscover that knowledge anew in each instance. Moreover, the knowledge can be formulated according to standard templates, such that a newly-hired or inexperienced participant can easily retrieve it and thus attain expert performance without the very significant time and effort that would otherwise have been involved in learning that knowledge from the start.
Furthermore, if such a system is constructed to use an active data store as a repository of knowledge, rules-based processing can be applied to the data to structure and relate elements of raw data into usable structured information. Changes to data and to the rules can trigger rules-based processing to change the data and the information structures as circumstances in the real world of the insurance business change, thus maintaining an accurate informational reflection of reality. Typically, now, the data store is associated with input mechanisms for accepting and applying changes to data and rules that have been entered by a local or remote user or passed in by an application program, a rules engine adapted to directly modify data by applying rules, and output mechanisms for passing information to users and applications, such as report writers or transactional programs. The input mechanisms conventionally contain validation or filtering mechanisms to check the validity of the data, both in terms of syntax and in terms of consistency of semantics. For example, they may test the syntax of the input to ensure that it is well-formed with respect to the syntactic structure of the database. They may also carry out some semantic validation to check that the data is meaningful, as, for example, checking that Alice is older than Bob by comparing the age attribute data values for Bob and Alice, before it will allow the input to be applied to the database.
The input mechanisms may also require other forms of validation of any existing data or rules that are to be changed. For example, they may be constrained when in communication with commercial applications, to ensure that changes are recoverable by cooperating with a recovery logging system. Similarly, they may be constrained when in communication with a transaction processing system, to abide by the rules for transactional changes to databases. When in communication with a workflow system, cooperation may be needed with the workflow system in ensuring that changes made during long-running workflows are compensable.
However, in an active entity-relationship-attribute database, in which rule-based processing is taking place to dynamically add, modify or delete data (and possibly rules) according to changes in circumstances—new relationship linkages, for example, being derived from rule-based reasoning about existing structures, content or rules—structures or meanings may become ill-formed as a result of internally-generated changes, which causes problems in subsequently deriving meaningful information from the database. The problem is particularly acute when cascades of changes are made to the data as a result of the sequential application of rules, the first application being triggered by a new input and each subsequent application triggered by a previous change. It is easy to see that validation of the initial input is not sufficient to maintain order in the database. Heretofore, a solution to the aforementioned problems has been thought to be one which would require the addition of greater and greater complexity to the rules engine and to the various input mechanisms to enforce validations during internally-generated changes as well as externally-generated changes. It is quite a complex system design and programming task to provide meta-rules to control the validation of rule-based changes to data. It is an even more complex system design and programming task to provide meta-meta-rules to control the validation of rule-based changes to rules.
It is believed, therefore, that a data storage system which provides the many advantages taught herein would obviate many of the problems and limitations described hereinabove, and would constitute a significant advancement in the art.